A Generalized Enhanced Quantum Fuzzy Approach for Efficient Data Clustering

Abstract

Data clustering is a challenging task to gain insights into data in various fields. In this paper, an Enhanced Quantum-Inspired Evolutionary Fuzzy C-Means (EQIE-FCM) algorithm is proposed for data clustering. In the EQIE-FCM, quantum computing concept is utilized in combination with the FCM algorithm to improve the clustering process by evolving the clustering parameters. The improvement in the clustering process leads to improvement in the quality of clustering results. To validate the quality of clustering results achieved by the proposed EQIE-FCM approach, its performance is compared with the other quantum-based fuzzy clustering approaches and also with other evolutionary clustering approaches. To evaluate the performance of these approaches, extensive experiments are being carried out on various benchmark datasets and on the protein database that comprises of four superfamilies. The results indicate that the proposed EQIE-FCM approach finds the optimal value of fitness function and the fuzzifier parameter for the reported datasets. In addition to this, the proposed EQIE-FCM approach also finds the optimal number of clusters and more accurate location of initial cluster centers for these benchmark datasets. Thus, it can be regarded as a more efficient approach for data clustering. INDEX TERMS Clustering, quantum computing, evolutionary algorithm, fuzzy set theory, bioinformatics.

Quantum-Inspired Clustering for Hazardous Asteroid Prediction in Quantum Machine Learning

“…This gate specifically supports the exploration of a large quantum search space, enabling the algorithm to avoid local optima and converge towards the global optimum. The rotation gate's unique ability to incrementally adjust the Q-bit states makes it particularly suitable for the evolutionary aspect of the EQIE-FCM algorithm, where gradual improvements are sought over several generations [53] . Tested on multiple datasets, including the Pima Indians Diabetes dataset, the EQIE-FCM algorithm has proven to perform better than many baseline methods, making it an effective method for clustering [53] .…”

“…The rotation gate's unique ability to incrementally adjust the Q-bit states makes it particularly suitable for the evolutionary aspect of the EQIE-FCM algorithm, where gradual improvements are sought over several generations [53] . Tested on multiple datasets, including the Pima Indians Diabetes dataset, the EQIE-FCM algorithm has proven to perform better than many baseline methods, making it an effective method for clustering [53] . In summary, EQIE-FCM is a sophisticated clustering algorithm that combines fuzzy logic, quantum computing concepts, and evolutionary strategies to efficiently discover structures within datasets.…”

“…The research paper [53] the Enhanced Quantum Fuzzy C-Means (EQIE-FCM) algorithm for data clustering. A review of the literature is part of the study, and it addresses the relevance of fuzzy logic and evolutionary algorithms with quantum inspiration for data clustering.…”

Quantum Machine Learning for Security Assessment in the Internet of Medical Things (IoMT)

“…"Quantum-enhanced machine learning" means using quantum algorithms to solve problems in machine learning. This makes traditional machine-learning techniques [38] more effective and, in many cases, speeds up the process [39][40][41][42]. When giving a classical dataset to a quantum computer for use, it is often encrypted for use in quantum information processing.…”

Fuzzy Logic on Quantum Annealers

“…The quantum-inspired algorithms are not specific to fuzzy systems, as it also exists for some other computational intelligence methods, e.g. quantum-inspired genetic algorithm [18], quantum fuzzy C-means data clustering [19] and quantuminspired neuro-fuzzy systems [20]. Some other research works around quantum-inspired computational intelligence are comprehensively reviewed in [21].…”